Abstract-Traditionally, simultaneous localization and mapping (SLAM) algorithms solve the localization and mapping problem in explored regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental-structure predictor to predict the structure inside an unexplored region (i.e., look-ahead mapping). The prediction process is based on the observation of the surroundings of an unexplored region and comparing it with the built map of explored regions. If a similar environment/structure is matched in the map of explored regions, a hypothesis is generated to indicate that a similar structure has been explored before. If the environment has repeated structures, the mobile robot can use the predicted structure as a virtual mapping, and decide whether or not to explore the unexplored region to save the exploration time. If the mobile robot decides to explore the unexplored region, a correct prediction can be used to speed up the SLAM process and build a more accurate map. We have also derived the Bayesian formulation of P-SLAM to show its compact recursive form for real-time operation. We have experimentally implemented the proposed P-SLAM on a Pioneer 3-DX mobile robot using a Rao-Blackwellized particle filter in real time. Computer simulations and experimental results validated the performance of the proposed P-SLAM and its effectiveness in indoor environments.Index Terms-Bayes procedures, environmental-structure prediction, simultaneous localization and mapping (SLAM).

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Traditionally, the SLAM problem solves the localization and mapping problem in explored and sensed regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental structure predictor to predict the structure inside an unexplored region (i.e., lookahead mapping). The prediction process is based on the observation of the surroundings of an unexplored region and comparing it with the built map of explored regions. If a similar structure is matched in the map of explored regions, a hypothesis is generated to indicate that a similar structure has been explored before. If the environment has repeated structures, the mobile robot can utilize the predicted structure as a virtual mapping, and decide whether or not to explore the unexplored region to save exploration time. If the mobile robot decides to explore the unexplored region, a correct prediction can be utilized to localize the robot and speed up the SLAM process. We also derive the Bayesian formulation of P-SLAM to show its compact recursive form for real-time operation. We have experimentally implemented the proposed P-SLAM in a Pioneer 3-DX mobile robot using a Rao-Blackwellized particle filter in real-time. Computer simulations and experimental results validated the performance of the proposed P-SLAM and its effectiveness in an indoor environment.

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